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    "# Introduction to Quantitative Finance\n",
    "\n",
    "Copyright (c) 2019 Python Charmers Pty Ltd, Australia, <https://pythoncharmers.com>. All rights reserved.\n",
    "\n",
    "<img src=\"img/python_charmers_logo.png\" width=\"300\" alt=\"Python Charmers Logo\">\n",
    "\n",
    "Published under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. See `LICENSE.md` for details.\n",
    "\n",
    "Sponsored by Tibra Global Services, <https://tibra.com>\n",
    "\n",
    "<img src=\"img/tibra_logo.png\" width=\"300\" alt=\"Tibra Logo\">\n",
    "\n",
    "\n",
    "## Module 1.5: Bayesian inference\n",
    "\n",
    "### 1.5.2 Basics of Bayesian inference\n",
    "\n",
    "Bayesian inference allows us to incorporate existing knowledge into our analysis. This previous knowledge is known as a *prior*, and will affect our results. Choosing a good prior, however, is not that important if you have lots of data. With data, your prior becomes just the starting point, with a minimal effect on the results.\n",
    "\n",
    "Such *a priori* information can come from population statistics, estimations, or other sources. Using a population statistic, or a default (all outcomes equally likely) is a normal starting place.\n",
    "\n",
    "### Example 1: Medical diagnosis\n",
    "\n",
    "Guido wakes up with spots all over his face. What is the probability he has smallpox?\n",
    "\n",
    "We can't observe the causes (they are latent), only the data. Bayes' theorem allows us to invert this to make inferences about the unknown causes.\n",
    "\n",
    "From public medical statistics we know that 90% of people with smallpox have spots, whereas 80% of people with chickenpox have spots. This gives us the following likelihoods:\n",
    "\n",
    "$$p(\\textrm{spots | smallpox}) = 0.9$$\n",
    "\n",
    "$$p(\\textrm{spots | chickenpox}) = 0.8$$\n",
    "\n",
    "From population statistics gathered, we also know that 50 people in every 1000 have spots and that 1 in every million people have smallpox.\n",
    "\n",
    "### Bayes' theorem\n",
    "\n",
    "Let $D$ be the observed data and $M$ be a certain model for the data (or hypothesis).\n",
    "\n",
    "Bayes' theorem can be derived from the laws of probability. It states:\n",
    "\n",
    "$$\n",
    "p(M | D) = p(M) \\times \\frac{p(D|M)}{p(D)}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\textrm{posterior} = \\textrm{prior } \\times \\frac{\\textrm{likelihood}}{\\textrm{ evidence}}\n",
    "$$\n",
    "\n",
    "### Likelihood\n",
    "\n",
    "The \"likelihood of smallpox\" is the conditional probability of spots (the data) given that the patient has smallpox (the model / diagnosis). In other words, if we assume the outcome, what is the probability of seeing this data?"
   ]
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   "source": [
    "def likelihood(data, model):\n",
    "    \"\"\"\n",
    "    Conditional probability of the data ('spots' / 'no spots') given the model\n",
    "    ('smallpox' / 'not smallpox').\n",
    "    \"\"\"\n",
    "    if data == 'spots':\n",
    "        return {'smallpox': 0.9, 'not smallpox': 0.05}[model]\n",
    "    else:\n",
    "        return {'smallpox': 0.1, 'not smallpox': 0.95}[model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "0.9"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "likelihood('spots', 'smallpox')  # If a patient has small pox, there is a 90% chance they have spots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prior\n",
    "\n",
    "The \"prior\" reflects our \"prior knowledge\" expressed as a background level of belief. What is the background prevalence of smallpox in the general population?\n",
    "\n",
    "Generally, and without seeing any data, not many people have smallpox. If you checked a random person, it is *much* more likely they would not have small pox. Note that this value ignores any data (like the presence of spots)."
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [],
   "source": [
    "def prior(model):\n",
    "    return {'smallpox': 1e-6, 'not smallpox': 1 - 1e-6}.get(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The evidence and posterior have definitions in terms of each other (but for the normalization flag). The evidence function tells us the probability of seeing the given evidence."
   ]
  },
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   "cell_type": "code",
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   "source": [
    "def evidence(data):\n",
    "    \"\"\"\n",
    "    The proportion of people in the general population who have spots or no spots\n",
    "    \"\"\"\n",
    "    return (posterior('smallpox', data, normalize=False) +\n",
    "            posterior('not smallpox', data, normalize=False))"
   ]
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   "execution_count": 6,
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   "source": [
    "def posterior(model, data, normalize=True):\n",
    "    \"\"\"\n",
    "    Posterior probability of the model ('smallpox / 'not smallpox')\n",
    "    given the data ('spots' / 'no spots')\n",
    "    \"\"\"\n",
    "    numerator = likelihood(data, model) * prior(model)\n",
    "    if not normalize:\n",
    "        return numerator\n",
    "    else:\n",
    "        return numerator / evidence(data)"
   ]
  },
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   "execution_count": 7,
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    {
     "data": {
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       "0.05000085"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "evidence('spots')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.799969400520191e-05"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior('smallpox', 'spots', normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    {
     "data": {
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       "1.052632520776466e-07"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior('smallpox', 'no spots', normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    {
     "data": {
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       "0.9999820003059948"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "posterior('not smallpox', 'spots', normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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    {
     "data": {
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "posterior('not smallpox', 'no spots', normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercises\n",
    "\n",
    "1. Check that substituting $x$ above correctly leads to the sum of both model probabilities (given spots and given no spots) to be 1\n",
    "2. We learn from public medical records that 80% of people with chickenpox have spots and that 1 in every 10,000 people have chickenpox. (As before, 1 in every 20 people have spots.)\n",
    "3. Suppose $x$ (the probability of spots without chickenpox) were much higher, like among teenagers. How would this affect the posterior probability of chickenpox? First, consider this intuitively, then run the code again to check your understanding.\n",
    "\n",
    "Repeat the above steps for chickenpox."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "attributes": {
     "classes": [],
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     "n": "44"
    }
   },
   "source": [
    "*For the solution to exercise 1, see `solutions/check_normalized.py`*\n",
    "\n",
    "*For the solution to exercise 2 see `solutions/public_medical.py`*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question does it matter for normalization what the value of $x$ is?\n",
    "\n",
    "Try a few values using `conditional_sum.subs(x, value)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You'll need to run the solution in this notebook to continue\n",
    "%run -i solutions/public_medical.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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    {
     "data": {
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       "0.00039988003598920324"
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior('chickenpox', 'spots', x=0.5)  # If 50% of those with spots do not have chicken pox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model comparison\n",
    "\n",
    "If we want to compare two models or hypotheses for the given data, we can compare the ratio of posterior probabilities (also known as posterior odds) between the two hypotheses.\n",
    "\n",
    "Here, with hypotheses $\\theta_c$ (Guido has chickenpox) and $\\theta_s$ (Guido has smallpox):\n",
    "\n",
    "$$\n",
    "R_{\\textrm{posterior}} = \\frac{p(\\theta_c | x)}{p(\\theta_s | x)}\n",
    "$$\n",
    "\n",
    "If we apply Bayes' rule, the marginal likelihoods cancel, so:\n",
    "\n",
    "$$\n",
    "R_\\textrm{posterior} = \\frac{p(x | \\theta_c)}{p(x | \\theta_s)} \\frac{p(\\theta_c)}{p(\\theta_s)}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\textrm{posterior odds} = \\textrm{Bayes factor} \\times \\textrm{prior odds}\n",
    "$$\n",
    "\n",
    "### Bayesian hypothesis testing\n",
    "\n",
    "We can do Bayesian hypothesis testing by calculating the Bayes factor, prior odds, and posterior odds for the hypothesis of chickenpox vs smallpox.\n",
    "\n",
    "\n",
    "### Example 2: flipping coins\n",
    "\n",
    "**Question:** How would you decide how unfair a coin is, based on just two coin flips?\n",
    "\n",
    "Define $p(\\theta)$ as the probability that the coin has a given bias $\\theta$. Define bias of 1 to be *always heads* and a bias of 0 to be *always tails*.\n",
    "\n",
    "Start with the likelihood, $p(x | \\theta)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,

   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "50"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "102"
    }
   },
   "outputs": [],
   "source": [
    "def likelihood(data, theta):\n",
    "    \"\"\"\n",
    "    Conditional probability of the data (x heads out of N coin tosses),\n",
    "    given the bias theta of the model (potentially biased).\n",
    "    \"\"\"\n",
    "    x, N = data\n",
    "    return scipy.stats.binom(N, theta).pmf(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "96"
    }
   },
   "outputs": [],
   "source": [
    "N = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "97"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20613038097752087"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihood([5, 15], 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will choose a prior. Let's go with a Beta prior. (Look up \"conjugate priors\".)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "98"
    }
   },
   "outputs": [],
   "source": [
    "def prior(theta, a, b):\n",
    "    return scipy.stats.beta(a, b).pdf(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In a beta distribution with a = 1, b = 2, there is a 75% chance of seeing less than 50% heads.\n",
    "scipy.stats.beta(1, 2).cdf(.5) - scipy.stats.beta(1, 2).cdf(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "99"
    }
   },
   "outputs": [],
   "source": [
    "params = [0.5, 1, 2, 3]\n",
    "x = np.linspace(0, 1, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "100"
    }
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run plot_priors.ipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "Which do you think best matches the belief that the coin is:\n",
    "1. more likely to be fair than biased\n",
    "1. just as likely to be biased as fair\n",
    "1. probably biased towards heads\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*For solutions, see `solutions/best_coin.py`*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, how to incorporate the data?\n",
    "\n",
    "Once we have our prior, we now collect data. This data is then used to update our model, better informing our outcome.\n",
    "\n",
    "### Evidence / marginal posterior\n",
    "\n",
    "The evidence for the data is now: $$p(\\textrm{data}) = \\int_{0}^1 d\\theta \\space p(\\textrm{data | }\\theta) p(\\theta)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "60"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.integrate import quad\n",
    "\n",
    "def evidence(data):\n",
    "    def lik(theta):\n",
    "        return likelihood(data, theta)\n",
    "    def post(theta):\n",
    "        return lik(theta) * prior(theta, alpha, beta)\n",
    "    evidence, abserr = quad(post, 0, 1, )\n",
    "    return evidence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now, we will just fix these: (\"just as likely biased as fair\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "59"
    }
   },
   "outputs": [],
   "source": [
    "(a, b) = (1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "58"
    }
   },
   "outputs": [],
   "source": [
    "trials = [0, 1, 2, 3, 4, 8, 16, 32, 50, 150]\n",
    "data = [0, 1, 1, 1, 1, 4, 6, 9, 13, 48]\n",
    "theta = np.linspace(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "63"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 4.33764125e-43, 1.42583319e-29, 4.50574362e-22,\n",
       "       4.75557907e-17, 2.15466932e-13, 1.30523859e-10, 1.93544556e-08,\n",
       "       1.00674662e-06, 2.31423249e-05])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lik(theta):\n",
    "    return likelihood((data[-1], trials[-1]), theta)\n",
    "lik(theta)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "64"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009392766325310331"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evidence((data[-1], trials[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "67"
    }
   },
   "outputs": [],
   "source": [
    "def posterior(model, data, normalize=True):\n",
    "    \"\"\"\n",
    "    Posterior probability of the model given the data\n",
    "    \"\"\"\n",
    "    numerator = likelihood(data, model) * prior(model, a, b)\n",
    "    if not normalize:\n",
    "        return numerator\n",
    "    else:\n",
    "        return numerator / evidence(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "68"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5956869799149338"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior(0.4, (15, 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "69"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bf6eb90350>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(theta, [posterior(t, (35, 50)) for t in theta])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_params = ((2, 2), (1, 1), (1, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run show_posteriors_coins.ipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result\n",
    "\n",
    "The result of Bayesian inference is a posterior **probability distribution**. This gives us more information than just the single most probable model or hypothesis; it also gives us information about the error associated with each choice.\n",
    "\n",
    "### Credible intervals\n",
    "\n",
    "A convenient way to summarize the spread of the posterior distribution is with a \"credible interval\" of \"Highest Posterior Density (HPD). This is the shortest interval containing a given portion of the probability density (such as 50%, 95% or 98%).\n",
    "\n",
    "#### Extended Exercise\n",
    "Implement this function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hpd(posterior, alpha=0.05):\n",
    "    \"\"\"\n",
    "    A (1 - α) credible interval\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    posterior : function\n",
    "        A posterior probability density function (normalized)\n",
    "    \n",
    "    alpha : float\n",
    "        A significance level (of sorts)\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "        A tuple (lower, upper) giving the lower and upper bounds for\n",
    "        the (1 - α) credible interval\n",
    "    \"\"\"\n",
    "    \n",
    "    ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See `solutions/credible_interval.py` for solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might try using `scipy.integrate.quad`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diachronic Interpretation\n",
    "\n",
    "The \"Diachronic Interpretation\" of Bayesian statistics is the application of the key equation over time, specifically as we collect new data. This allows us to update the likelihood of our probabilities.\n",
    "\n",
    "We saw it above with our coin flips. As we flip more coins, we collect more data, giving us a greater understanding of whether the coin is fair or not.\n",
    "\n",
    "When performing a Diachronic Interpretation, you want your hypotheses to have the following two properties:\n",
    "\n",
    "1. the hypotheses are mutually exclusive and only one can be true, and\n",
    "2. the hypotheses are exhaustive, and no other possibility is possible.\n",
    "\n",
    "\n",
    "You can definitely perform a Bayesian analysis when the above properties are not true, but the analysis is harder and more care needs to be taken in your analysis.\n",
    "\n",
    "Specifically, we rewrite Bayes formula with $H$ and $D$:\n",
    "\n",
    "$P(H|D) = \\frac{P(H)P(D|H)}{P(D)}$\n",
    "\n",
    "Where:\n",
    "\n",
    "* P(H|D) is the probability of the hypothesis we are interested in, given we have seen the data $D$, called the **posterior**.\n",
    "* P(H) is the probability of the hypothesis itself, given no data was seen. A normal approach here is to simply consider all hypothesis likely, unless you have some other reasonable value. This is known as the **prior**.\n",
    "* P(D|H) is the probability we would see the data, assuming the hypothesis is true, known as the **likelihood**.\n",
    "* P(D) is the probability we would see the data, and is a **normalisation factor**.\n",
    "\n",
    "\n",
    "That last value, $P(D)$ is the same for all hypotheses we would be considering, meaning that we could simply not compute it. This happens in the Naive Bayes algorithm in machine learning, and has no detremental impact on the decision making process, but it does mean that the values you get after applying Bayes theorem are not actually probabilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extended exercise\n",
    "\n",
    "Start with the following two hypotheses:\n",
    "\n",
    "1. The gold price is correlated with the (1 day lagged) GBP/USD conversion rate.\n",
    "2. The gold price is uncorrelated with the GPB/USD conversion rate.\n",
    "\n",
    "Potential data sources:\n",
    "* Gold prices: https://www.quandl.com/data/LBMA/GOLD-Gold-Price-London-Fixing\n",
    "* GBP/USD: https://www.quandl.com/data/BUNDESBANK/BBEX3_D_GBP_USD_CM_AC_000-Exchange-Rates-For-The-Us-Dollar-In-The-United-Kingdom-Gbp-1-Usd-middle\n",
    "\n",
    "Using a diachronic interpretation of Bayes Theorem, which of the stated hypothesis is true?\n",
    "\n",
    "Some pointers to get you started:\n",
    "\n",
    "1. Create a definition for \"is correlated\" before you start. You may choose, for example, \"if $R^2 > 0.7$\"\n",
    "1. Get both datasets, clean them up and create a single DataFrame with the values we are interested in\n",
    "1. Perform a Linear Regression using the previous techniques and review the confidence interval and $R^2$ data. While this is unrelated to the Bayes theorem, you can use your result here to give you an expectation on the results of this exercise. \n",
    "1. The prior probability can simply be 50% for each theorem, as without data, we don't really have any way to change from this.\n",
    "1. The probability of the data does not need to be computed for comparing hypothesis"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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